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Most law firms have bought AI tools. Few have changed how the work actually gets done. Models are interchangeable. Your house positions, your judgement calls and your deal history are not. An ontology links that knowledge to matters, clients and documents, so it compounds with every deal instead of walking out of the door. In the recording, Tarek Nseir and Chris Lormor take a fund formation matter from term sheet to first LPA draft, route the off-position clauses to a partner for approval, then carry those obligations into a later acquisition. In this recorded demo, we show you exactly how it works.

Most AI for law stalls in the same place. The model doesn’t know how your firm works. An ontology fixes that. A live map of your house positions, your matters, your clients and your documents, and what each deal has committed you to. We walk through five steps on a fund formation matter. Reasoning and sources on every clause. Off-position clauses routed to a partner for approval. Approved documents become trackable obligations. A later acquisition flags co-investment rights above the agreed threshold. Repeated departures trigger a review of the position itself. In this recorded demo, we show you exactly how it works.

Most AI for law stalls in the same place. The model doesn’t know how your firm works. An ontology fixes that. A live map of your house positions, your matters, your clients and your documents, and what each deal has committed you to. We walk through five steps on a fund formation matter. Reasoning and sources on every clause. Off-position clauses routed to a partner for approval. Approved documents become trackable obligations. A later acquisition flags co-investment rights above the agreed threshold. Repeated departures trigger a review of the position itself. In this recorded demo, we show you exactly how it works.

88% of organisations use AI in at least one part of the business, yet only 37% see a return from it. That figure has barely moved in a year, and the answer is usually already inside the building. Within a typical organisation, someone may have cut an afternoon of research to half an hour, or automated their weekly admin. Those skills stay on their desk, unshared and untested by peers. The challenge is letting colleagues use someone else's skills and prompts safely and consistently. Our founder Rad Parvin argues that as long as AI stays mostly with a small group, the return stays limited. The skills become tested tools, and the enthusiasts become advocates the whole team can learn from. Read Rad's full piece at the link below. https://lnkd.in/e42G-THn

What's stopping enterprise Al from scaling past the pilot stage? In his latest piece for the Forbes Technology Council, our co-founder Tarek Nseir argues the constraint is context. Agents go into production before anyone has given them a model of the business to reason over. As firms push towards Al at scale, the edge goes to whoever has done the harder work of building the layer underneath. An ontology, a knowledge graph, and the reasoning frameworks that carry the rules behind real decisions.

Most law firms have bought a point solution this year. One of the largest law firms in the US took a different route: a $500 million investment to build an AI ontology, a structured model of the firm's own knowledge that gets more valuable every time it's used, instead of a licence that expires. You don't need a $500 million budget to apply the same principle, only the right approach. Join Valliance's Tarek Nseir and Chris Lormor on 30 September as we explain what that means in practice, and what it would take to build the same advantage at your firm. 📅 30 September, 14:30 BST 🔗 Register here: https://lnkd.in/eYtVBMdR